Medical image classification method for combining deep characteristic extraction and shallow characteristic extraction

A deep feature, medical image technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as low accuracy rate and poor classification algorithm effect, achieve high automatic classification, overcome the effect of poor effect

Inactive Publication Date: 2016-11-23
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to avoid the poor effect of the classification algorithm based on deep learning caused by the small number of medical images in the prior art, and the low correct rate of the traditional classification method based on visual features, the present invention proposes a combination of deep feature extraction and shallow Medical Image Classification Method Based on Feature Extraction

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  • Medical image classification method for combining deep characteristic extraction and shallow characteristic extraction

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Embodiment Construction

[0024] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0025] The present invention provides an innovative method for simultaneously performing feature extraction based on deep convolutional neural network and heuristic feature extraction guided by domain knowledge, and combining the obtained two types of features to train a classifier to realize medical image classification.

[0026] This method extracts several small image blocks from each medical image, and the category of each image block is the category of the image it is in, so that the classification problem based on the image is transformed into the classification problem based on the image block. First, use the image blocks extracted from all training images to train a deep convolutional neural network model using the stochastic gradient descent method, and select the output of the fully connected layer of the network as the descriptive features of the corres...

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Abstract

The invention relates to a medical image classification method for combining deep characteristic extraction and shallow characteristic extraction. The medical image classification method comprises the following steps: training a deep convolutional neural network model, a bag-of-word model and a BP neural network at first, then dividing a medical image to be classified into image sub blocks, inputting the image sub blocks into the deep convolutional neural network model, the bag-of-word model and the BP neural network, which are trained, in sequence to obtain the type of each image sub block, and classifying the medical image to be classified based on the majority voting principle.

Description

technical field [0001] The invention relates to an image classification method, especially for medical image classification problems with large intra-class differences (such as CT images from different parts of the body) and small inter-class differences (such as CT images and MRI images). The feature extraction of the convolutional neural network and the visual feature extraction guided by domain knowledge, and the combination of the obtained deep and shallow features to train the classifier, better achieve medical image classification. Background technique [0002] Image classification mainly includes two main links: feature extraction and classifier construction. Among them, feature extraction aims to transform the image into a set of numerical features that are convenient for subsequent processing, which is the basis of classifier construction and is particularly important in solving image classification problems. Traditional visual features generally include image colo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24
Inventor 夏勇张建鹏
Owner NORTHWESTERN POLYTECHNICAL UNIV
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